AIBullisharXiv – CS AI · 8h ago7/10
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Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset
Researchers developed an explainable machine learning model using XGBoost to detect Alzheimer's disease stages from routine clinical assessments, achieving 98.2% accuracy on three-class classification (normal cognition, mild cognitive impairment, and Alzheimer's disease). The model uses SHAP analysis to provide interpretable feature importance, identifying clinical biomarkers like CDR Global and MMSE as key predictors.